The Robot that Learns from the Therapist How to Assist Stroke Patients

Abstract

Results from clinical studies suggest that assisted training is beneficial for the recovery of functioning in patients with stroke and other central nervous system injuries. The training consists of the repetition of movements, which change the excitability of the brain, and due to cortical plasticity have carry-over effects. We are developing a 3D arm assistant that interfaces the patient at the hand/wrist. The development addresses three major issues: (1) the selection of the tasks that are appropriate for the training based on the level of motor abilities (2) the design of the visual feedback that enhances the motivation to train, and (3) the assessment of the performance. Therefore, our design integrates the new 3D robot assistant, various gaming based visual feedback, and software that acquires data on-line from sensors (position of the hand and force between the robot and the hand). The major novelties that the 3D arm assistant brings are the following: an automatic method of capturing movements presented by the therapist (expert), the use of the probabilistic movement representation for control of the robot, the incorporation of simple gaming with adjustable levels of difficulty, and finally, the assessment of differences between the achieved and target movements (kinematics) and interface force measured by a special handle with multiple sensors. The components of the new arm assistant in 2D have been tested and proved to work effectively in the clinical trials with stroke patients.